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The lung environment controls alveolar macrophage metabolism and responsiveness in type 2 inflammation

Nature Immunology volume 20, pages 571–580 (2019)Cite this article

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Abstract

Fine control of macrophage activation is needed to prevent inflammatory disease, particularly at barrier sites such as the lungs. However, the dominant mechanisms that regulate the activation of pulmonary macrophages during inflammation are poorly understood. We found that alveolar macrophages (AlvMs) were much less able to respond to the canonical type 2 cytokine IL-4, which underpins allergic disease and parasitic worm infections, than macrophages from lung tissue or the peritoneal cavity. We found that the hyporesponsiveness of AlvMs to IL-4 depended upon the lung environment but was independent of the host microbiota or the lung extracellular matrix components surfactant protein D (SP-D) and mucin 5b (Muc5b). AlvMs showed severely dysregulated metabolism relative to that of cavity macrophages. After removal from the lungs, AlvMs regained responsiveness to IL-4 in a glycolysis-dependent manner. Thus, impaired glycolysis in the pulmonary niche regulates AlvM responsiveness during type 2 inflammation.

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Fig. 1: AlvMs are unresponsive to IL-4.
Fig. 2: AlvMs are less responsive than IntMs to IL-4c administered directly into the airways.
Fig. 3: AlvMs are less responsive than IntMs during helminth infection.
Fig. 4: The pulmonary niche regulates AlvM responsiveness to IL-4 independently of host commensals.
Fig. 5: AlvMs and PECMs display different metabolic gene profiles.
Fig. 6: Impaired uptake and use of glucose renders AlvMs unresponsive to IL-4.

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Code availability

Bioinformatics analyses were performed with publicly available code from bioconductor.org.

Data availability

The data that support the findings of this study are available from the corresponding author upon request. RNA-seq data were deposited at Gene Expression Omnibus, with the following accession code: GSE126309.

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Acknowledgements

We thank members of the MCCIR and MacDonald laboratory (University of Manchester) for scientific discussions and some experimental assistance. We thank J. Allen (University of Manchester) for critical reading of the manuscript, the University of Manchester Single Cell Facility for flow cytometry, cell sorting and ImageStream, K. Couper and J. Grainger (University of Manchester) for provision of Pep3 and CX3CR1eGFP mice and M. Travis for providing recombinant TGF-β. This research was supported by a MCCIR PhD studentship (F.R.S.), the Medical Research Council (grant no. MR/P026907/1, H.C. and J.M.), the National Institutes of Health (grant no. HL080396 and HL130938, C.M.E.), the Wellcome Trust Institutional Strategic Support Fund (grant no.105610, R.K.G., D.J.T. and M.Z.K.), Medical Research Foundation UK joint funding with Asthma UK (grant no. MRFAUK-2015–302, T.E.S.), BBSRC studentship (C.S.), a University of Manchester Dean’s Prize Early Career Research Fellowship (P.C.C.), Springboard Award (Academy of Medical Sciences, grant no. SBF002/1076, P.C.C.) and MCCIR core funding (A.S.M. and T.H.). This work was also made possible through use of the Manchester Gnotobiotic Facility that was established with the support of the Wellcome Trust (grant no. 097820/Z/11/B), using founder mice obtained from the Clean Mouse Facility, University of Bern, Switzerland. The Bioimaging Facility microscopes used in this study were purchased with grants from BBSRC, Wellcome Trust and the University of Manchester Strategic Fund.

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Author notes

  1. Freya R. Svedberg

    Present address: Laboratory of Myeloid Cell Ontogeny and Functional Specialisation, VIB Center for Inflammation Research, Ghent, Belgium

  2. Freya R. Svedberg

    Present address: Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

Authors and Affiliations

  1. Lydia Becker Institute of Immunology and Inflammation, Manchester Collaborative Centre for Inflammation Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

    Freya R. Svedberg, Sheila L. Brown, Gareth J. Howell, Tara E. Sutherland, Tracy Hussell, Peter C. Cook & Andrew S. MacDonald

  2. Lydia Becker Institute of Immunology and Inflammation, Wellcome Trust Centre for Cell-Matrix Research, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

    Maria Z. Krauss, Laura Campbell, Catherine Sharpe, David J. Thornton & Richard K. Grencis

  3. AstraZeneca, Discovery Sciences IMED, Gothenburg, Sweden

    Maryam Clausen

  4. Department of Child Health, Division of Clinical and Experimental Sciences, Faculty of Medicine, Sir Henry Wellcome Laboratories, Southampton General Hospital, University of Southampton, Southampton, UK

    Howard Clark & Jens Madsen

  5. Institute for Life Sciences, University of Southampton, Southampton, UK

    Howard Clark & Jens Madsen

  6. National Institute for Health Research, Southampton Respiratory Biomedical Research Unit, Southampton Centre for Biomedical Research, University Hospital Southampton NHS Foundation Trust, Southampton, UK

    Howard Clark & Jens Madsen

  7. Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA

    Christopher M. Evans

  8. Institute of Immunology and Infection Research, Centre for Immunity, Infection and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK

    Alasdair C. Ivens

  9. AstraZeneca RIA IMED, Gothenburg, Sweden

    Danen M. Cunoosamy

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Contributions

P.C.C. and A.S.M. were responsible for conceptualization. F.R.S., S.L.B., M.Z.K., L.C., M.C., G.H., A.C.I. and P.C.C. contributed methodology. F.R.S., S.L.B., M.Z.K., L.C., C.S., M.C., G.H., T.E.S., A.C.I. and P.C.C. conducted investigations. H.C., J.M., C.M.E., T.E.S., D.J.T., R.K.G., D.M.C., T.H. and A.S.M. provided resources. F.R.S., P.C.C. and A.S.M. wrote the first draft. F.R.S., S.L.B., C.M.E., A.C.I., D.J.T., R.K.G., D.M.C., T.H., P.C.C. and A.S.M. reviewed, edited and wrote the final article. D.J.T., R.K.G., D.M.C., T.H., P.C.C. and A.S.M. were involved in funding acquisition.

Corresponding authors

Correspondence to Peter C. Cook or Andrew S. MacDonald.

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Competing interests

The Manchester Collaborative Centre for Inflammation Research is a joint venture between the University of Manchester, AstraZeneca and GSK. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Integrated supplementary information

Supplementary Figure 1 Flow cytometry gating strategy for the identification of pulmonary myeloid cells, M(IL-4)-responsive cells and macrophages from various tissue sites.

a, Representative flow cytometry plots showing gating for cells isolated from naïve lung tissue to identify neutrophil (CD11b+Ly6G+ - Ly6G included in the lineage antibody cocktail), IntM (MerTK+CD64+CD11b+Siglec-F-), AlvM (MerTK+CD64+CD11b-Siglec-F+), eosinophil (MerTK-Siglec-F+), dentritic cells (MerTK-CD11c+MHCII+) and monocyte (MerTK-CD64+CD11b+Ly6C+) populations. Data representative of 8 independent experiments. b, RELMα and Ki67 expression, or EdU incorporation, by lung tissue AlvMs and IntMs, or PECMs, on d4 following i.p. PBS or IL-4c administration on d0 and d2, and EdU injection i.p. 3 h before tissue collection. Data representative of 8–10 independent experiments. c, Expression of CD11b and Siglec-F on macrophages (MerTK+CD64+) from PEC, PLEC, liver, colon or lung tissue. Data representative of 2–9 independent experiments.

Supplementary Figure 2 Alveolar macrophages have high basal expression of Ym1, pSTAT6, mTORC1 and mTORC2.

a–d, Histograms of Ym1, pSTAT6, pAkt T308 and pAkt S473 (indicators of mTORC1 and mTORC2 activity, respectively) amounts in lung tissue AlvMs or PECMs from naïve WT or Il4ra-/- mice. Data representative of 2 independent experiments, n = 6 (a), n = 3 (b–d) mice per group. Data analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test for multiple comparisons, displayed as mean ± s.e.m., *P < 0.05 and **P < 0.01.

Supplementary Figure 3 Alveolar macrophages are less responsive than are interstitial macrophages during N. brasiliensis infection.

a, Flow cytometry plots showing the gating strategy for cells isolated from lung tissue of naïve mice, or on d2 and d7 following infection s.c. with 500 L3 N. brasiliensis larvae, to identify AlvMs, IntMs and eosinophils. Data representative of 4 independent experiments. b, Flow cytometry plots showing RELMα, Arginase1, Ym1 and Ki67 expression by lung tissue AlvMs and IntMs from naïve or infected mice. Data representative of 1–4 independent experiments. c, Quantification of the percentage of Arginase1+ and Ym1+ lung tissue AlvMs and IntMs from naïve or infected mice, n = 4 (naïve), n = 3 (d2), n = 3 (d7) mice per group. Data analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test for multiple comparisons, displayed as mean ± s.e.m., *P < 0.05, **P < 0.01 and ****P < 0.0001.

Supplementary Figure 4 Identification of alveolar macrophages based solely on expression of Siglec-F and CD11c may include contaminating interstitial macrophages and eosinophils.

a, Representative flow cytometry plots depicting a traditional gating strategy to identify AlvMs on the basis of Siglec-F and CD11c, showing the proportion of these cells that are AlvMs, IntMs or eosinophils, on the basis of expression of CD64 and MerTK (as defined in the gating strategy in Supplementary Fig. 1a), for cells isolated from lung tissue of naïve mice, or on d2 and d7 following infection s.c. with 500 L3 N. brasiliensis larvae. Data representative of 4 independent experiments. b, Proportion of AlvMs (green) and contaminating IntMs (purple) in the Siglec-F+CD11c+ gate. Data representative of 4 independent experiments, n = 4 (naïve), n = 4 (d2), n = 3 (d7) infected mice per group. c, Proportion of AlvMs, IntMs and eosinophils (red) in the Siglec-F+CD11c+ gate. Data representative of 4 independent experiments, n = 4 (naïve), n = 4 (d2), n = 3 (d7) infected mice per group. d, Representative flow cytometry plots and quantification of CD11c+Siglec-F+ cells that were positive for RELMα. Pie charts demonstrate the significant proportion of these RELMα+ cells that were IntMs or eosinophils, rather than AlvMs. Data pooled from 2 independent experiments, n = 8 (naïve), n = 8 (d2), n = 6 (d7) infected mice per group. e, Representative flow cytometry plots from 4 independent experiments showing FSC and SSC characteristics of gated populations, and overlays demonstrating the inability to remove only eosinophils based solely on high granularity. f, Representative flow cytometry plots from 4 independent experiments depicting overlapping expression of Siglec-F, CD11b and CD11c by AlvMs, IntMs and eosinophils. Data analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test for multiple comparisons, displayed as mean ± s.e.m., ***P < 0.001.

Supplementary Figure 5 The responses of alveolar and interstitial macrophages to IL-4c are independent of CD200R1, Muc5b and SP-D regulation.

a, CD200R expression on lung tissue AlvMs and IntMs from naïve WT mice. Histograms representative of 3 independent experiments. b, Quantification of the percentage of RELMα+ and Ki67+ lung tissue AlvMs and IntMs from WT or Cd200r1-/- mice on d4 following i.p. PBS or IL-4c administration on d0 and d2. Data representative of 3 independent experiments, n = 4 mice per group (WT AlvM PBS, Cd200r1-/- AlvM PBS, WT AlvM IL-4c, WT IntM PBS, Cd200r1-/- IntM PBS, WT IntM IL-4c), n = 3 mice per group (Cd200r1-/- AlvM IL-4c, Cd200r1-/- IntM IL-4c). c, Representative immunofluorescence images (left) of Muc5b (green) expression in lung sections from WT mice on d4 following i.p. PBS or IL-4c administration on d0 and d2, with quantification of epithelial Muc5b expression (right). Scale bar: 50μM. Data representative of 2 independent experiments, n = 4 (PBS), n = 6 (IL-4c treated) mice per group. d, Quantification of the percentage of RELMα+ and Ki67+ lung tissue AlvMs and IntMs from WT or Muc5b-/- mice on d4 following i.p. PBS or IL-4c administration on d0 and d2. Data representative of 3 independent experiments, n = 4 (WT AlvM PBS, WT IntM PBS), n = 3 (WT AlvM IL-4c, Muc5b-/- AlvM IL-4c, WT IntM IL-4c, Muc5b-/- IntM IL-4c), n = 2 (Muc5b-/- AlvM PBS, Muc5b-/- IntM PBS) mice per group. e, Quantification of the percentage of RELMα+ and Ki67+ lung tissue AlvMs and IntMs from WT or Sfptd-/- mice on d4 following i.p. PBS or IL-4c administration on d0 and d2. Data representative of 3 independent experiments, n = 2 (WT AlvM PBS, WT IntM PBS), n = 3 (WT AlvM IL-4c, WT IntM IL-4c, Sfptd-/- AlvM PBS, Sfptd-/- AlvM IL-4c, Sfptd-/- IntM PBS, Sfptd-/- IntM IL-4c) mice per group. Data analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test for multiple comparisons, displayed as mean ± s.e.m..

Supplementary Figure 6 Pathway analysis of peritoneal cavity, interstitial and alveolar macrophages from mice given injection of IL-4c versus those given injection of PBS and of alveolar macrophages versus peritoneal cavity macrophages.

KEGG pathway analysis as determined by RNA-seq of significantly altered mRNA transcripts of a, PECMs, b, IntMs or c, AlvMs isolated from PEC or lung tissue by flow cytometry on d4 following i.p. PBS or IL-4c administration on d0 and d2. d, KEGG pathway analysis of significantly altered transcripts of lung tissue AlvMs versus PECMs from IL-4c treated mice, as determined by RNA-seq. Pathways highlighted in bold represent cellular metabolic processes. X-axis: -log10 hypergeometric test (enrichment) P value, line represents the -log10 equivalent of P = 0.05. Data generated from 2–3 independent biological replicates per group, each biological replicate generated from a pool of 3–5 mice.

Supplementary Figure 7 Alveolar macrophages display lower respiratory capacity than that of peritoneal macrophages, and neither fatty acid oxidation nor TGF-β is a vital factor in the recovery of alveolar macrophage M(IL-4) activation.

a, OCR of AlvMs and PECMs isolated from lung tissue or PEC of naïve mice by flow cytometry, at baseline and after sequential treatment (vertical lines) with oligomycin, FCCP, antimycin A and rotenone to measure oxidative phosphorylation (left). Quantification of spare respiratory capacity (right). Graphs show individual replicate wells, n = 3 (AlvM), n = 10 (PECM) wells per group, each group pooled cells from 8 mice. Data representative of 2 independent experiments. b & c, Quantification of Retnla, Arg1 and Chil3 transcript amounts in lung tissue AlvMs from naïve mice cultured for 48 h in media alone or with rIL-4 (20 ngml-1), (b) ± etomoxir (200 µM,) or (c) ± rTGF-β (10 ngml-1) as determined by qPCR. AU arbitrary units. Graphs show individual replicate wells (3 wells per group, each group pooled cells from 8 mice). Data analyzed by one-way analysis of variance (ANOVA) with Tukey’s post-test for multiple comparisons test, displayed as mean ± s.e.m., ***P < 0.001.

Supplementary information

Supplementary Information

Supplementary Figures 1–7 and Supplementary Table 8: Primers for RT-qPCR

Reporting Summary

Supplementary Table 1: Differentially upregulated expressed genes between PECMs isolated from IL-4c treated mice versus PBS treated mice.

This table displays differentially upregulated expressed genes generated via RNAseq analysis between PECMΦs isolated from IL-4c treated versus PBS-treated mice. The list shows 1,265 significantly upregulated transcripts (P < 0.01).

Supplementary Table 2: Downregulated gene expression between PECMs isolated from IL-4c treated versus PBS treated mice.

This table displays differentially downregulated expressed genes generated via RNAseq analysis between PECMΦs isolated from IL-4c treated versus PBS-treated mice. The list shows 806 significantly downregulated transcripts (P < 0.01).

Supplementary Table 3: Upregulated gene expression between IntMs isolated from IL-4c treated versus PBS treated mice.

This table displays differentially upregulated expressed genes generated via RNAseq analysis between IntMΦs isolated from IL-4c treated versus PBS treated mice. The list shows 80 significantly upregulated transcripts (P < 0.01).

Supplementary Table 4: Downregulated gene expression between IntMs isolated from IL-4c treated versus PBS treated mice.

This table displays differentially downregulated expressed genes generated via RNAseq analysis between IntMΦs isolated from IL-4c treated versus PBS treated mice. The list shows 27 significantly downregulated transcripts (P <0 .01).

Supplementary Table 5. Downregulated gene expression between AlvMs isolated from IL-4c treated versus PBS treated mice.

This table displays differentially down-regulated expressed genes generated via RNAseq analysis between AlvMs isolated from IL-4c treated versus PBS treated mice. The list shows 2 significantly downregulated transcripts (P<0.01).

Supplementary Table 6: Upregulated gene expression between AlvMs versus PECMs isolated from IL-4c treated mice.

This table displays differentially upregulated expressed genes generated via RNAseq analysis between AlvMs versus PECMs isolated from IL-4c treated mice. The list shows the top 1,000 significantly downregulated transcripts (P < 0.01).

Supplementary Table 7: Downregulated gene expression between AlvMs versus PECMs isolated from IL-4c treated mice.

This table displays differentially downregulated expressed genes generated via RNAseq analysis between AlvMs versus PECMs isolated from IL-4c treated mice. The list shows the top 1,000 significantly down-regulated transcripts (P < 0.01).

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Svedberg, F.R., Brown, S.L., Krauss, M.Z. et al. The lung environment controls alveolar macrophage metabolism and responsiveness in type 2 inflammation. Nat Immunol 20, 571–580 (2019). https://doi.org/10.1038/s41590-019-0352-y

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